Biliary Atresia (BA) is the most serious liver disease of childhood and the most common indication for pediatric liver transplantation. This progressive sclerosing cholangiopathy, however, remains difficult to diagnose and to manage. In addition, the pathophysiology and etiology of BA remains poorly understood. Because some infants with BA benefit from immediate surgical treatment with a Kasai portoenterostomy, diagnostic methods to identify infants with BA more rapidly and with greater sensitivity and specificity are needed. With the advent of new proteomic technology and new computer based image analysis and predictive statistical algorithms, recent dramatic advances have been made in the ability to use serum protein profile analysis to segregate patients by disease state or prognosis. We propose to analyze human serum samples from infants with BA and neonatal hepatitis (NH) using a combination of fluorescent two dimensional gel electrophoresis, computer assisted image analysis, automated protein spot picking and proteolytic digestion followed by protein identification using sequencing mass spectroscopy. We will then use computer based statistical algorithms for data analysis to identify protein pattems that predict which infants have BA and which infants have NH. In parallel with these studies, we have established a mouse model system to examine the effect of bile duct ligation on the abundance of serum proteins. This model system will provide insight about which serum protein changes in infants with biliary atresia result primarily from biliary tract obstruction. Our studies of human infant serum are greatly facilitated by our participation in the recently created Biliary Atresia Research Consortium (BARC). This collaborative study by 9 major pediatric medical centers will collect patient clinical data and biological samples that will be analyzed by many BARC associated investigators. Although the samples will be coded to protect patient identity, the fact that many different types of research will be performed by different groups on samples and clinical data from the same patients will greatly add to the value of our proteomic analyses. [unreadable] [unreadable] [unreadable]